Suchergebnisse

Analysis of whether post truth narratives register as a fundamental threat to democratic cultures in anglo-American context. ; This piece assesses the risk of disinformation primarily, but not exclusively, in the Anglo-American context. It unpicks assumptions behind post-truth and fake news; considers precedents for disinformation and queries the extent of its novelty. Are these manageable challenges to democratic cultures or a crisis? It concludes that whatever the terminological tangles, industrialized disinformation signal threats to the public sphere, threats underscored by historical events highlighting the vulnerability of democracy. Yet threats to democratic systems have not deleted their scrutinizing capabilities from below (voters) and from above (the legislature). Therefore challenges, for all their potency and potential, have not yet reached crisis.

Air pollution continues to be a key health issue in Scotland, despite recent improvements in concentrations. The Scottish Government published the Cleaner Air For Scotland strategy in 2015, and will introduce Low Emission Zones (LEZs) in the four major cities (Aberdeen, Dundee, Edinburgh and Glasgow) by 2020. However, there is no epidemiological evidence quantifying the current health impact of air pollution in Scotland, which this paper addresses. Additionally, we estimate the health benefits of reducing concentrations in city centres where most LEZs are located. We focus on cardio-respiratory disease and total non-accidental mortality outcomes, linking them to concentrations of both particulate (PM 10 and PM 2.5 ) and gaseous (NO 2 and NO x ) pollutants. Our two main findings are that: (i) all pollutants exhibit significant associations with respiratory disease but not cardiovascular disease; and (ii) reducing concentrations in city centres with low resident populations only provides a small health benefit.

ABSTRACT
ObjectivesThe use of "real-time" data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland's Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI).
This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making.
ApproachWe demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence.
ResultsIn the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression.
ConclusionDespite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making.

In this review we illustrate our view on the epidemiological relevance of geographically mapping cancer mortality. In the first part of this work, after delineating the history of cancer mapping with a view on interpretation of Cancer Mortality Atlases, we briefly illustrate the 'art' of cancer mapping. Later we summarise in a non-mathematical way basic methods of spatial statistics. In the second part of this paper, we employ the 'Atlas of Cancer Mortality in the European Union and the European Economic Area 1993-1997' in order to illustrate spatial aspects of cancer mortality in Europe. In particular, we focus on the cancer related to tobacco and alcohol epidemics and on breast cancer which is of particular interest in cancer mapping. Here we suggest and reiterate two key concepts. The first is that a cancer atlas is not only a visual tool, but it also contain appropriate spatial statistical analyses that quantify the qualitative visual impressions to the readers even though at times revealing fallacy. The second is that a cancer atlas is by no means a book where answers to questions can be found. On the contrary, it ought to be considered as a tool to trigger new questions.

We thank the EAVE II Patient Advisory Group for their support. EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant reference MC_PC_20058). Additional support has been provided through Public Health Scotland and Scottish Government DG Health and Social Care and the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation. ; Peer reviewed ; Publisher PDF

ABSTRACTObjectivesPrior use of antimicrobials, in particular broad-spectrum antimicrobials, is associated with the development of Clostridium difficile infection (CDI). Our previous work has demonstrated increased risk with cumulative exposure but there is limited evidence on specific patterns of cumulative antimicrobial prescribing prior to infection. Understanding this pattern will help inform antimicrobial stewardship. This study aims to investigate the prescribing patterns for CDI cases with more than 4 weeks cumulative antimicrobial exposure during the 6 months prior to their CDI date.ApproachWe linked three Scottish patient-level data sets: laboratory confirmed CDI (ECOSS), prescriptions for antimicrobials in primary care (PIS) and all hospital admissions (SMR01). From ECOSS all cases of CDI reported in the period August 2010 to July 2013 were identified. Each CDI case was linked to SMR01 to allocate case type (either hospital or community associated) and to PIS for previous antimicrobial prescriptions. Visual representation of temporal exposure indicating both duration and drug type of each antimicrobial dispensed during the 6 month period before the infection was produced to clearly understand the pattern of prescribing.ResultsIn the study period, there were a total of 1557 community acquired CDI cases without recent (prior 3 months) hospitalisation. Among them, 287 (18%) cases had more than 4 weeks prior cumulative antimicrobial exposure accumulating a total of 1311 dispensed prescriptions. Cases had an average 4 (Q1:3,Q3:6) prescriptions and 2 (Q1:2,Q3:3) different types of antimicrobials dispensed. The timeline plot shows that repeated short duration prescriptions contributed more to cumulative exposure than long duration prophylaxis. The most common antimicrobials prescribed were amoxicillin (38% of cases), trimethoprim (30%) and flucloxacillin (27%). The median duration per prescription for each of these was less than 2 weeks. The antimicrobials that had a median duration of around a month per prescription were oxytetracycline (3%) and clindamycin (6%).
ConclusionsThis study of a national linked patient level data set contained sufficient numbers of cases to enable investigation of the prescribing pattern in individuals with highest risk cumulative exposure. This study uses NHS Scotland's developing Infection Intelligence Platform which will place Scotland as a world leader in the use of health informatics to support infection control and antimicrobial stewardship.

We thank Jackie Caldwell, Iain Bishop, Grant Wyper, Kjersti Fergusson, and Martyn Ritchie in NHS National Services Scotland Information Services Division and all members of the Advisory Group for their contribution. Funding: The study was funded by Scottish Government Chief Scientist Office project grant CZH/3/17. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. ST works at the Health Services Research Unit, University of Aberdeen, which is funded by the Chief Scientist Office of the Scottish Government Health Directorates. ; Peer reviewed ; Publisher PDF

Introduction The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm. Methods and analysis We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell's C, Brier Score, R 2 and Royston's D. Ethics and dissemination Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal.